зеркало из https://github.com/microsoft/LightGBM.git
Removing the string to decimal conversion for float
We dont need it we aren't doing any computation with float values. We print out whatever values are read from the LightGBM_model.txt as a string.
This commit is contained in:
Rakib Hasan
Родитель
1b7643ba60
Коммит
fa15332e3b
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@ -1,6 +1,6 @@
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PMML Generator
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==============
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The script pmml.py can be used to translate the LightGBM models, found in LightGBM_model.txt, to predictive model markup language (PMML). These models can then be imported by other analytics applications. The models that the language can describe includes decision trees. The specification of PMML can be found here at the Data Mining Group's [website](http://dmg.org/pmml/v4-3/GeneralStructure.html).
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The script pmml.py can be used to translate the LightGBM models, found in LightGBM_model.txt, to predictive model markup language (PMML). These models can then be imported by other analytics applications. The models that the language can describe includes decision trees. The specification of PMML can be found here at the Data Mining Group's [website](http://dmg.org/pmml/v4-3/GeneralStructure.html).
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In order to generate pmml files do the following steps.
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```
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170
pmml/pmml.py
170
pmml/pmml.py
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@ -1,16 +1,12 @@
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from __future__ import print_function
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from builtins import map
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from builtins import next
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from decimal import Decimal
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import sys
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import os
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import traceback
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def unique_id():
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global unique_node_id
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nid = unique_node_id
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unique_node_id += 1
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return nid
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import itertools
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def get_value_string(line):
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@ -22,11 +18,7 @@ def get_array_strings(line):
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def get_array_ints(line):
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return map(lambda x: int(x), line[line.index('=') + 1:].split())
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def get_array_floats(line):
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return map(lambda x: Decimal(x), line[line.index('=') + 1:].split())
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return list(map(int, line[line.index('=') + 1:].split()))
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def get_field_name(node_id, prev_node_idx, is_child):
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@ -73,7 +65,7 @@ def print_nodes_pmml(**kwargs):
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(
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"<Node id=\"{0}\" score=\"{1}\" " +
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" recordCount=\"{2}\">").format(
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unique_id(),
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next(unique_id),
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score,
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recordCount),
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file=pmml_out)
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@ -116,8 +108,8 @@ def print_pmml(pmml_out):
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(feature), file=pmml_out)
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print("\t\t\t\t\t</MiningSchema>", file=pmml_out)
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# begin printing out the decision tree
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print("\t\t\t\t\t<Node id=\"%d\" score=\"%s\" recordCount=\"%d\">" %
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(unique_id(), internal_value[0], internal_count[0]), file=pmml_out)
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print("\t\t\t\t\t<Node id=\"{0}\" score=\"{1}\" recordCount=\"{2}\">".format(
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next(unique_id), internal_value[0], internal_count[0]), file=pmml_out)
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print("\t\t\t\t\t\t<True/>", file=pmml_out)
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print_nodes_pmml(
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node_id=left_child[0],
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@ -139,80 +131,78 @@ if len(sys.argv) != 2:
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sys.exit(0)
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# open the model file and then process it
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try:
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with open(sys.argv[1]) as model_in:
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model_content = filter(
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lambda line: line != '',
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model_in.read().strip().split('\n'))
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objective = get_value_string(model_content[4])
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sigmoid = Decimal(get_value_string(model_content[5]))
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feature_names = get_array_strings(model_content[6])
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model_content = model_content[7:]
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line_no = 0
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segment_id = 1
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with open(sys.argv[1], 'r') as model_in:
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model_content = [l for l in model_in.read().splitlines() if l]
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with open('LightGBM_pmml.xml', 'w') as pmml_out:
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print(
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"<PMML version=\"4.3\" \n" +
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"\t\txmlns=\"http://www.dmg.org/PMML-4_3\"\n" +
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"\t\txmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"\n" +
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"\t\txsi:schemaLocation=\"http://www.dmg.org/PMML-4_3 http://dmg.org/pmml/v4-3/pmml-4-3.xsd\"" +
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">",
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file=pmml_out)
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print("\t<Header copyright=\"Microsoft\">", file=pmml_out)
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print("\t\t<Application name=\"LightGBM\"/>", file=pmml_out)
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print("\t</Header>", file=pmml_out)
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# print out data dictionary entries for each column
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print(
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"\t<DataDictionary numberOfFields=\"%d\">" %
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len(feature_names), file=pmml_out)
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# not adding any interval definition, all values are currently
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# valid
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for feature in feature_names:
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print(
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"\t\t<DataField name=\"" +
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feature +
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"\" optype=\"continuous\" dataType=\"double\"/>",
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file=pmml_out)
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print("\t</DataDictionary>", file=pmml_out)
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print("\t<MiningModel functionName=\"regression\">", file=pmml_out)
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print("\t\t<MiningSchema>", file=pmml_out)
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# list each feature name as a mining field, and treat all outliers
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# as is, unless specified
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for feature in feature_names:
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print(
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"\t\t\t<MiningField name=\"%s\"/>" %
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(feature), file=pmml_out)
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print("\t\t</MiningSchema>", file=pmml_out)
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print(
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"\t\t<Segmentation multipleModelMethod=\"sum\">",
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file=pmml_out)
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# read each array that contains pertinent information for the pmml
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# these arrays will be used to recreate the traverse the decision
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# tree
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while model_content[line_no][:4] == 'Tree':
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print("\t\t\t<Segment id=\"%d\">" % segment_id, file=pmml_out)
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print("\t\t\t\t<True/>", file=pmml_out)
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tree_no = model_content[line_no][5:]
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num_leaves = int(get_value_string(model_content[line_no + 1]))
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split_feature = get_array_ints(model_content[line_no + 2])
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threshold = get_array_floats(model_content[line_no + 4])
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decision_type = get_array_ints(model_content[line_no + 5])
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left_child = get_array_ints(model_content[line_no + 6])
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right_child = get_array_ints(model_content[line_no + 7])
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leaf_parent = get_array_ints(model_content[line_no + 8])
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leaf_value = get_array_floats(model_content[line_no + 9])
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leaf_count = get_array_ints(model_content[line_no + 10])
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internal_value = get_array_floats(model_content[line_no + 11])
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internal_count = get_array_ints(model_content[line_no + 12])
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unique_node_id = 0
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print_pmml(pmml_out)
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print("\t\t\t</Segment>", file=pmml_out)
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line_no += 13
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segment_id += 1
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objective = get_value_string(model_content[4])
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sigmoid = Decimal(get_value_string(model_content[5]))
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feature_names = get_array_strings(model_content[6])
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model_content = model_content[7:]
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segment_id = 1
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print("\t\t</Segmentation>", file=pmml_out)
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print("\t</MiningModel>", file=pmml_out)
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print("</PMML>", file=pmml_out)
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except Exception as ioex:
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print(ioex)
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with open('LightGBM_pmml.xml', 'w') as pmml_out:
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print(
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"<PMML version=\"4.3\" \n" +
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"\t\txmlns=\"http://www.dmg.org/PMML-4_3\"\n" +
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"\t\txmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"\n" +
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"\t\txsi:schemaLocation=\"http://www.dmg.org/PMML-4_3 http://dmg.org/pmml/v4-3/pmml-4-3.xsd\"" +
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">",
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file=pmml_out)
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print("\t<Header copyright=\"Microsoft\">", file=pmml_out)
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print("\t\t<Application name=\"LightGBM\"/>", file=pmml_out)
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print("\t</Header>", file=pmml_out)
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# print out data dictionary entries for each column
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print(
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"\t<DataDictionary numberOfFields=\"%d\">" %
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len(feature_names), file=pmml_out)
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# not adding any interval definition, all values are currently
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# valid
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for feature in feature_names:
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print(
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"\t\t<DataField name=\"" +
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feature +
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"\" optype=\"continuous\" dataType=\"double\"/>",
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file=pmml_out)
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print("\t</DataDictionary>", file=pmml_out)
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print("\t<MiningModel functionName=\"regression\">", file=pmml_out)
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print("\t\t<MiningSchema>", file=pmml_out)
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# list each feature name as a mining field, and treat all outliers
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# as is, unless specified
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for feature in feature_names:
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print(
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"\t\t\t<MiningField name=\"%s\"/>" %
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(feature), file=pmml_out)
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print("\t\t</MiningSchema>", file=pmml_out)
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print(
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"\t\t<Segmentation multipleModelMethod=\"sum\">",
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file=pmml_out)
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# read each array that contains pertinent information for the pmml
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# these arrays will be used to recreate the traverse the decision
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# tree
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model_content = iter(model_content)
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tree_start = next(model_content)
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while tree_start[:4] == 'Tree':
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print("\t\t\t<Segment id=\"%d\">" % segment_id, file=pmml_out)
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print("\t\t\t\t<True/>", file=pmml_out)
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tree_no = tree_start[5:]
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num_leaves = int(get_value_string(next(model_content)))
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split_feature = get_array_ints(next(model_content))
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split_gain = next(model_content)
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threshold = get_array_strings(next(model_content))
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decision_type = get_array_ints(next(model_content))
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left_child = get_array_ints(next(model_content))
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right_child = get_array_ints(next(model_content))
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leaf_parent = get_array_ints(next(model_content))
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leaf_value = get_array_strings(next(model_content))
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leaf_count = get_array_strings(next(model_content))
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internal_value = get_array_strings(next(model_content))
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internal_count = get_array_strings(next(model_content))
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tree_start = next(model_content)
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unique_id = itertools.count(1)
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print_pmml(pmml_out)
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print("\t\t\t</Segment>", file=pmml_out)
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segment_id += 1
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print("\t\t</Segmentation>", file=pmml_out)
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print("\t</MiningModel>", file=pmml_out)
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print("</PMML>", file=pmml_out)
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